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Add Conv2d for CPU (#14388)
* Conv2D: Add CPU version * Half decent * Tiled approach for F32 * remove file * Fix tests * Support F16 operations * add assert about size * Review: further formatting fixes, add assert and use CPU version of fp32->fp16
This commit is contained in:
Aman Gupta
GitHub
parent
745f11fed0
commit
0a5a3b5cdf
5 changed files with 250 additions and 3 deletions
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@ -3,6 +3,7 @@
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#include "ggml-cpu.h"
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#include "ggml-impl.h"
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#include "binary-ops.h"
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#include "ggml.h"
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#include "unary-ops.h"
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#include "vec.h"
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@ -6545,6 +6546,186 @@ void ggml_compute_forward_im2col_back_f32(
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}
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}
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static void ggml_call_mul_mat(ggml_type type, const ggml_compute_params * params, int64_t m, int64_t n, int64_t k,
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void * a, void * b, float * c) {
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const ggml_type_traits * traits = ggml_get_type_traits(type);
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struct ggml_tensor src1 = {};
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src1.type = type;
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src1.ne[0] = k;
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src1.ne[1] = m;
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src1.ne[2] = 1;
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src1.ne[3] = 1;
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src1.nb[0] = traits->type_size;
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src1.nb[1] = k * traits->type_size;
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src1.nb[2] = src1.nb[1];
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src1.nb[3] = src1.nb[2];
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src1.data = a;
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struct ggml_tensor src0 = {};
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src0.type = type;
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src0.ne[0] = k;
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src0.ne[1] = n;
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src0.ne[2] = 1;
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src0.ne[3] = 1;
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src0.nb[0] = traits->type_size;
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src0.nb[1] = k * traits->type_size;
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src0.nb[2] = src0.nb[1];
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src0.nb[3] = src0.nb[2];
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src0.data = b;
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struct ggml_tensor dst = {};
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dst.ne[0] = n;
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dst.ne[1] = m;
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dst.ne[2] = 1;
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dst.ne[3] = 1;
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dst.nb[0] = sizeof(float);
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dst.nb[1] = n * sizeof(float);
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dst.nb[2] = dst.nb[1];
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dst.nb[3] = dst.nb[2];
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dst.data = c;
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dst.src[0] = &src0;
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dst.src[1] = &src1;
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ggml_compute_forward_mul_mat(params, &dst);
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}
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// ggml_compute_forward_conv_2d
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static void ggml_compute_forward_conv_2d_impl(const ggml_compute_params * params,
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const ggml_tensor * kernel, // [KW, KH, IC, OC]
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const ggml_tensor * src, // [W, H, C, N]
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ggml_tensor * dst, // [OW, OH, OC, N]
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ggml_type kernel_type) {
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GGML_ASSERT(ggml_is_contiguous(kernel));
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GGML_ASSERT(kernel_type == GGML_TYPE_F16 || kernel_type == GGML_TYPE_F32);
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GGML_ASSERT(kernel->type == kernel_type);
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const ggml_type_traits * traits = ggml_get_type_traits(kernel_type);
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const int32_t stride_x = dst->op_params[0];
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const int32_t stride_y = dst->op_params[1];
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const int32_t pad_x = dst->op_params[2];
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const int32_t pad_y = dst->op_params[3];
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const int32_t dilation_x = dst->op_params[4];
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const int32_t dilation_y = dst->op_params[5];
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const int64_t c_in = src->ne[2];
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const int64_t c_out = kernel->ne[3];
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GGML_ASSERT(c_in == kernel->ne[2]);
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const int64_t src_w = src->ne[0];
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const int64_t src_h = src->ne[1];
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const int64_t knl_w = kernel->ne[0];
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const int64_t knl_h = kernel->ne[1];
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const int64_t dst_w = dst->ne[0];
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const int64_t dst_h = dst->ne[1];
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const float * src_data = (float *) src->data;
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void * knl_data = kernel->data;
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float * dst_data = (float *) dst->data;
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const int64_t knl_n = knl_w * knl_h * c_in;
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const int64_t patch_total = dst->ne[3] * dst_w * dst_h;
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const int64_t space_per_patch = knl_n * traits->type_size + c_out * sizeof(float);
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const int64_t batch_size = params->wsize / space_per_patch;
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const int64_t patches_per_batch = batch_size > 8 ? (batch_size / 8) * 8 : batch_size;
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const int64_t batch_n = (patch_total + patches_per_batch - 1) / patches_per_batch;
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GGML_ASSERT(patches_per_batch > 0 && batch_size >= 1);
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void * tmp = params->wdata;
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for (int64_t batch_i = 0; batch_i < batch_n; ++batch_i) {
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const int64_t patch_start_batch = batch_i * patches_per_batch;
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const int64_t patch_end_batch = std::min(patch_start_batch + patches_per_batch,
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patch_total);
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const int64_t patch_n = patch_end_batch - patch_start_batch;
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const int64_t patch_per_thread = (patch_n + params->nth - 1) / params->nth;
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const int64_t patch_start = patch_start_batch + params->ith * patch_per_thread;
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const int64_t patch_end = std::min(patch_start + patch_per_thread, patch_end_batch);
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//im2col for a patch
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for (int64_t p = patch_start; p < patch_end; ++p) {
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const int64_t batch_n = p / (dst_w * dst_h);
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const int64_t src_x = (p / dst_w) % dst_h;
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const int64_t src_y = p % dst_w;
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const float * src_base = (const float *)((const char *)src_data + batch_n * src->nb[3]);
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char * dst_row = (char *) tmp + (p % patches_per_batch) * knl_n * traits->type_size;
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for (int64_t ic = 0; ic < c_in; ++ic) {
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for (int64_t ky = 0; ky < knl_h; ++ky) {
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for (int64_t kx = 0; kx < knl_w; ++kx) {
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const int64_t sy = src_x * stride_y + ky * dilation_y - pad_y;
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const int64_t sx = src_y * stride_x + kx * dilation_x - pad_x;
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int64_t dst_idx = ic * (knl_h * knl_w) + ky * knl_w + kx;
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float src_val;
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if (sy < 0 || sy >= src_h || sx < 0 || sx >= src_w) {
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src_val = 0.0f;
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} else {
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const float * src_ptr = (const float *)((const char *)src_base + sx * src->nb[0] + sy * src->nb[1] + ic * src->nb[2]);
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src_val = *src_ptr;
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}
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char * element_ptr = dst_row + dst_idx * traits->type_size;
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if (kernel_type == GGML_TYPE_F32) {
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*(float *) element_ptr = src_val;
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} else if (kernel_type == GGML_TYPE_F16) {
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*(ggml_fp16_t *) element_ptr = GGML_CPU_FP32_TO_FP16(src_val);
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}
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}
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}
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}
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} // patches handled by this thread
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ggml_barrier(params->threadpool);
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float * gemm_output = (float *) ((char *) tmp + patches_per_batch * knl_n * traits->type_size);
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GGML_ASSERT(gemm_output + patch_n * c_out <= (float*)tmp + params->wsize);
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// GEMM: patches[patch_n, knl_n] × kernel[knl_n, c_out] = output[patch_n, c_out]
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ggml_call_mul_mat(kernel_type, params, patch_n, c_out, knl_n, tmp, knl_data, gemm_output);
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ggml_barrier(params->threadpool);
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//permute back [OC, N, OH, OW] to [N, OC, OH, OW]
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const int64_t permute_per_thread = (patch_n + params->nth - 1) / params->nth;
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const int64_t permute_start = params->ith * permute_per_thread;
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const int64_t permute_end = std::min(permute_start + permute_per_thread, patch_n);
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for (int64_t i = permute_start; i < permute_end; ++i) {
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const int64_t p = patch_start_batch + i;
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const int64_t batch_n = p / (dst_w * dst_h);
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const int64_t dst_y = (p / dst_w) % dst_h;
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const int64_t dst_x = p % dst_w;
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for (int64_t oc = 0; oc < c_out; ++oc) {
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const float value = gemm_output[i * c_out + oc];
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float * dst_ptr = (float *)((char *)dst_data + dst_x * dst->nb[0] + dst_y * dst->nb[1] + oc * dst->nb[2] + batch_n * dst->nb[3]);
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*dst_ptr = value;
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}
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}
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}
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}
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void ggml_compute_forward_conv_2d(
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const ggml_compute_params * params,
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ggml_tensor * dst) {
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const ggml_tensor * src0 = dst->src[0];
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const ggml_tensor * src1 = dst->src[1];
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ggml_compute_forward_conv_2d_impl(params, src0, src1, dst, src0->type);
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}
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// ggml_compute_forward_conv_transpose_2d
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void ggml_compute_forward_conv_transpose_2d(
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